Tackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE

The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassificati...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2019-11, Vol.662 (6), p.62011
Main Authors: Saifudin, A, Hendric, S W H L, Soewito, B, Gaol, F L, Abdurachman, E, Heryadi, Y
Format: Article
Language:English
Subjects:
Algorithms
Bagging
Datasets
Oversampling
Prediction models
Software
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Summary:The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassification. The ensemble technique using AdaBoost and Bagging algorithms. The results of the study show that the model that integrates SMOTE and Bagging provides better performance. The proposed model can find more software defects and more precise.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/662/6/062011